By Valentin Gies, Thierry M. Bernard (auth.), Eric Andres, Guillaume Damiand, Pascal Lienhardt (eds.)

ISBN-10: 3540255133

ISBN-13: 9783540255130

ISBN-10: 3540319654

ISBN-13: 9783540319658

This ebook constitutes the refereed court cases of the twelfth foreign convention on Discrete Geometry for computing device Imagery, DGCI 2005, held in Poitiers, France in April 2005.

The 36 revised complete papers awarded including an invited paper have been conscientiously reviewed and chosen from fifty three submissions. The papers are geared up in topical sections on functions, discrete hierarchical geometry, discrete tomography, discrete topology, item homes, reconstruction and popularity, doubtful geometry, and visualization.

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Additional info for Discrete Geometry for Computer Imagery: 12th International Conference, DGCI 2005, Poitiers, France, April 13-15, 2005. Proceedings

Example text

This last property is used to retrieve efficiently the set of points encoding a contour. The paper is thus organized as follows: We first present the main features of combinatorial pyramids (Section 2). Then, the specific advantages of this model within this framework are illustrated by a new hierarchical watershed construction scheme using specific features of combinatorial pyramids (Section 3). 2 Combinatorial Pyramids A combinatorial pyramid corresponds to a stack of successively reduced combinatorial maps where the initial combinatorial map G0 usually encodes a 4 connected planar sampling grid.

Figure 9 shows ΩR (Γ ) for a two-tasks system sharing a resource. While avoiding enumeration in the discrete model, we reach very efficient computation time. As a comparison, for a seven task system sharing four resources, computing the automaton model takes more than 2 hours while the computation of the discrete modele last less than 1 second. 32 ´ Andr`es G. Largeteau, D. Geniet, and E. τ1 time τ2 Fig. 9. System geometrical model:τ1 =(0,7,10,12), τ2 =(0,3,6,6) 4 Conclusion Validity spaces are useful to model hard real-time systems running on multiprocessor architectures and sharing resources.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(4):307–316, APRIL 1991. [14] L. Najman and M. Couprie. Watershed algorithms and contrast preservation. In Discrete geometry for computer imagery, volume 2886, pages 62–71. LNCS, Springer Verlag, 2003. [15] L. Najman and M. Schmitt. Geodesic saliency of watershed contours and hierarchical segmentation. IEEETPAMI, 18(2):1163–1173, December 1996. [16] C. Vachier and F. Meyer. A morphological scale-space approach to image segmentation based on connected operators.

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Discrete Geometry for Computer Imagery: 12th International Conference, DGCI 2005, Poitiers, France, April 13-15, 2005. Proceedings by Valentin Gies, Thierry M. Bernard (auth.), Eric Andres, Guillaume Damiand, Pascal Lienhardt (eds.)


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